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Integrated bulk and spatial transcriptomic analysis identifies progression-associated molecular signatures in biopsy-proven hypertensive nephropathy [RNA-seq]

GSE339456 Homo sapiens (human) RNA-seq (gene expression) 35 samples Submitted 2026/07/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 35 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
35 samples: 10 × FFPE, EarlyStable,n vs 10 × FFPE, EarlyProgressive,n vs 8 × FFPE, LateProgressive,n vs 7 × FFPE, LateStable,nGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Hypertensive nephropathy (HN) is a leading cause of chronic kidney disease (CKD), but molecular mechanisms underlying disease progression remain poorly understood. We aimed to identify transcriptomic signatures associated with disease progression and to localize these signatures within renal architectures. Patients with biopsy-proven HN were selected from the Norwegian Kidney Biopsy Registry and stratified after ≥5 years of follow-up into early stable (ES), early progressor (EP), late stable (LS) and late progressor (LP) based on baseline kidney function and annual eGFR decline.
Read the full summary ↓
Bulk transcriptomic profiling was performed in a discovery cohort and validated in an independent cohort using nCounter profiling. Spatial transcriptomics was applied to map progression-associated signatures to nephron compartments. Bulk RNA sequencing identified 415 differentially expressed genes (DEGs) between ES and EP, and 692 between LS and LP samples. ES was enriched in circadian rhythm pathways, with reduced NR1D1 and NR1D2 expression in EP. LP was associated with metabolic reprogramming including glutathione, retinol and amino acid metabolism. Random forest modelling identified NR1D1 gene expression as a key classifier, validated in an independent cohort. Spatial transcriptomics revealed nephron segment-specific expression patterns, including a proximal tubule cluster enriched in ferroptosis in LP. Comparative nephron-segment analyses across groups revealed progression-associated biomarkers, including CHGA, CCN2 and SFRP2 in glomeruli, and APOE in proximal tubules. HN progression appears to involve stage-specific and compartmentalized molecular programs. Integration of bulk and spatial transcriptomics identifies candidate biomarkers and pathways, potentially supporting innovative prognostic stratification and therapeutic strategies.
This dataset
Open GSE339456 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE339456_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1498894 and SRA study SRP720251. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
10 × FFPE, EarlyStable,n vs 10 × FFPE, EarlyProgressive,n vs 8 × FFPE, LateProgressive,n vs 7 × FFPE, LateStable,n

Supports a between-group comparison across 35 samples.

How we read this: 4 replicated groups read from 35 sample titles; they account for 35 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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